Machine Learning Driven Air Flow Control For Reduced Energy Consumption of Ships
Research Project, 2021
– 2023
This project is for the first time exploring the usage of machine learning to optimize the performance of ships and vessels.
The project is aiming to address the investigation and the control of the air flow surrounding longhaul ships. The external flow has an impact on the total ship resistance and it is also responsible for natural instabilities, created for example over aft frigate deck regions, that influence safety features, such helicopter landing pads, and environmental aspects, such as avoiding smoke intake to the HVAC systems. The main tangible goals are therefore to decrease drag (resistance of motion) by 5% and develop a system which have control over the natural flow instabilities. This will be achieved with a machine learning driven design approach to drive an active flow control system
Participants
Rickard Bensow (contact)
Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology
Sinisa Krajnovic
Chalmers, Mechanics and Maritime Sciences (M2)
Kewei Xu
Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology
Funding
Chalmers Transport Area of Advance
Funding Chalmers participation during 2021–2023
Related Areas of Advance and Infrastructure
Sustainable development
Driving Forces
Transport
Areas of Advance
Energy
Areas of Advance
C3SE (Chalmers Centre for Computational Science and Engineering)
Infrastructure
Innovation and entrepreneurship
Driving Forces